Beware of the simulated dag! causal discovery benchmarks may be easy to game
Simulated DAG models may exhibit properties that, perhaps inadvertently, render their
structure identifiable and unexpectedly affect structure learning algorithms. Here, we show …
structure identifiable and unexpectedly affect structure learning algorithms. Here, we show …
Learning latent causal graphs via mixture oracles
We study the problem of reconstructing a causal graphical model from data in the presence
of latent variables. The main problem of interest is recovering the causal structure over the …
of latent variables. The main problem of interest is recovering the causal structure over the …
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families
Greedy algorithms have long been a workhorse for learning graphical models, and more
broadly for learning statistical models with sparse structure. In the context of learning …
broadly for learning statistical models with sparse structure. In the context of learning …
Efficient Bayesian network structure learning via local Markov boundary search
We analyze the complexity of learning directed acyclic graphical models from observational
data in general settings without specific distributional assumptions. Our approach is …
data in general settings without specific distributional assumptions. Our approach is …
Estimating large causal polytrees from small samples
S Chatterjee, M Vidyasagar - arXiv preprint arXiv:2209.07028, 2022 - arxiv.org
We consider the problem of estimating a large causal polytree from a relatively small iid
sample. This is motivated by the problem of determining causal structure when the number …
sample. This is motivated by the problem of determining causal structure when the number …
Ocdaf: Ordered causal discovery with autoregressive flows
We propose OCDaf, a novel order-based method for learning causal graphs from
observational data. We establish the identifiability of causal graphs within multivariate …
observational data. We establish the identifiability of causal graphs within multivariate …
[图书][B] Compendium of Neurosymbolic Artificial Intelligence
If only it were possible to develop automated and trainable neural systems that could justify
their behavior in a way that could be interpreted by humans like a symbolic system. The field …
their behavior in a way that could be interpreted by humans like a symbolic system. The field …
Optimal estimation of Gaussian DAG models
We study the optimal sample complexity of learning a Gaussian directed acyclic graph
(DAG) from observational data. Our main results establish the minimax optimal sample …
(DAG) from observational data. Our main results establish the minimax optimal sample …
Partial homoscedasticity in causal discovery with linear models
J Wu, M Drton - IEEE Journal on Selected Areas in Information …, 2023 - ieeexplore.ieee.org
Recursive linear structural equation models and the associated directed acyclic graphs
(DAGs) play an important role in causal discovery. The classic identifiability result for this …
(DAGs) play an important role in causal discovery. The classic identifiability result for this …
Distributionally robust skeleton learning of discrete Bayesian networks
We consider the problem of learning the exact skeleton of general discrete Bayesian
networks from potentially corrupted data. Building on distributionally robust optimization and …
networks from potentially corrupted data. Building on distributionally robust optimization and …